6 citations · 10 across the 9 of their papers we have counts for
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Relation Extraction with Fine-Tuned Large Language Models in Retrieval Augmented Generation Frameworks
Sefika Efeoglu, Adrian Paschke
Information Extraction (IE) is crucial for converting unstructured data into structured formats like Knowledge Graphs (KGs). A key task within IE is Relation Extraction (RE), which…
Converter: Enhancing Interoperability in Research Data Management
Sefika Efeoglu, Zongxiong Chen, Sonja Schimmler +1
Research Data Management (RDM) is essential in handling and organizing data in the research field. The Berlin Open Science Platform (BOP) serves as a case study that exemplifies th…
A Continual Relation Extraction Approach for Knowledge Graph Completeness
Sefika Efeoglu
Representing unstructured data in a structured form is most significant for information system management to analyze and interpret it. To do this, the unstructured data might be co…
GraphMatcher: A Graph Representation Learning Approach for Ontology Matching
Sefika Efeoglu
Ontology matching is defined as finding a relationship or correspondence between two or more entities in two or more ontologies. To solve the interoperability problem of the domain…
Fine-Grained Named Entities for Corona News
Sefika Efeoglu, Adrian Paschke
Information resources such as newspapers have produced unstructured text data in various languages related to the corona outbreak since December 2019. Analyzing these unstructured…
Retrieval-Augmented Generation-based Relation Extraction
Sefika Efeoglu, Adrian Paschke
Information Extraction (IE) is a transformative process that converts unstructured text data into a structured format by employing entity and relation extraction (RE) methodologies…